Automatic Detection of Vascular Bundles and Corolla Contour in 3D Floral Image

• Main Authors: Yi-Hsiang Wang, 王奕翔
• Other Authors: Yan-Fu Kuo
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9zt248

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 106 === Corolla shape variation is an interesting theme for researchers. Three-dimensional (3D) images of corollas retain genuine information of the corollas and are optimum for studying corolla shape variation. However, labeling landmarks, which define the shape of a corolla, in a 3D image is labor-intensive. This study proposed a method to automatically detect the first-order veins and corolla contours in 3D corolla images. This study also examines the relationship between corolla shapes and pollinators for genus Sinningia. In the study, 3D images of 130 specimens from 28 species of the genus Sinningia were collected. Procedures were then developed to detect the first-order veins of the corollas using Hessian of Gaussian and Dijkstra’s algorithm and to detect the corolla contours using vector harmony and smoothing. The diversity of corolla shape was represented by means of first-order veins and corolla contour. The successful detection rate reached 86.54%. Two traits, contour-vein ratio and corolla angle, were defined and quantified from the first-order veins and corolla contours to realize the relationship between corolla shapes and pollinators. A choice of pollinators was certainly affected by the shape of a flower. Ornithophily species developed corollas with straight and narrow tubes to sift pollinators other than hummingbirds. Melittophily species developed corollas with a lobe that bends upward to cater to bees. The proposed method was also tested on species other than subtribe Ligeriinae (e.g., A. miresa, S. saxorum, and D. tamiana) to show the applicability to fresh sympetalous flowers. This study brought a new method for identifying flower features in 3D space compared with the existing approaches.